自定义模块kNN.py中定义了一个函数classify0,但主程序调用时总提示...

发布网友发布时间：2022-05-02 03:50

共3个回答

热心网友时间：2022-04-18 18:42

蛋疼的公司网络，看不到图片。首先确认下是否import成功了比如你 import kNN那你调用的时候要写 kNN.classify0不然就写 from kNN import *然后就可以直接调用了（默认你放在同个目录下）

热心网友时间：2022-04-18 20:00

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/1/19 14:33
# @Author : GanZiB
# @Email : ganzib4fun@163.com
# @Site :
# @File : kNN.py
# @Software: PyCharm
from numpy import *
import operator
import matplotlib
import matplotlib.pylab as plt

def createDataSet():
group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
labels = ['A', 'A', 'B', 'B']
return group, labels

def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = tile(inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances ** 0.5
sortedDisIndicies = distances.argsort()
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDisIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]

def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = zeros((numberOfLines, 3))
classLabelVector = []
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
try:
returnMat[index, :] = listFromLine[0:3]
except BaseException as e:
pass
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector

def autoNum(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
# normDataSet = zeros(shape(dataSet))
m = dataSet.shape[0]
normDataSet = dataSet - tile(minVals, (m, 1))
normDataSet = normDataSet / tile(ranges, (m, 1))
return normDataSet, ranges, minVals

def datingClassTest():
hoRatio = 0.10
datingDataMat, datingLabels = file2matrix("datingTestSet2.txt")
normMat, ranges, minVals = autoNum(datingDataMat)
m = normMat.shape[0]
numTestVecs = int(m * hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
print "the classifier came back with: %d the real answer is: %d" % (classifierResult, datingLabels[i])
if (classifierResult != datingLabels[i]): errorCount += 1.0
print "the total erro rate is: %f" % (errorCount / float(numTestVecs))

if __name__ == '__main__':
# returnMat, classLabelVector = file2matrix("datingTestSet2.txt")
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(returnMat[:, 0], returnMat[:, 1], 15.0 * array(classLabelVector), 15.0 * array(classLabelVector))
# plt.show()
datingClassTest()

热心网友时间：2022-04-18 21:35

你调用classfy0（）试一下。